Operation Of Air Conditioning Systems Research Articles

Air-conditioning load characteristics and grey box predicting model in subway stations

With the continuous expansion of metro, energy conservation of air-conditioning system in subway stations draws the attention of the whole world. Most of the researches are focused on improving the energy efficiency of the system, while the load characteristics of subway stations are paid little attention. The inaccurate load prediction in the designing of air-conditioning system results in redundancy and low operating efficiency. Therefore, accurately predicting the air-conditioning load is crucial for energy saving in subway stations. In this paper, an innovative three-stage subway air-conditioning load test method was proposed and implemented in nine subway stations in Qingdao. Based on the test data, the special air-conditioning load characteristics of subway stations as underground buildings were analyzed. The results showed that electrical equipment load accounted for the largest part, reaching 53.7 %, while only 42 % of the equipment power was converted into air-conditioning load. The positive role of the envelope should not be ignored, especially in the case of entrance and exit walls, which bore almost all of the fresh air load. Based on the three-stage test, a grey box model was established and the error was validated to be within 6 %. With this model, the load of air-conditioning system can be calculated by inputting passenger flow, power of electrical equipment and public area. This paper provides a reference for the design and operation of air-conditioning system in subway stations.

Analysis and development of criteria for the operational efficiency of urban electric buses

Problem. Currently, manufacturers and research specialists primarily utilize the amount of autonomous mileage and/or the specific electricity consumption of autonomous DEEs per unit of mileage to describe the general characteristics of urban electric buses. However, the structural mass and the key operational parameter, the nominal passenger capacity, are often disregarded. Therefore, comparing electric buses of the same type with identical dimensional parameters based solely on these criteria is illogical and incorrect. Goal. This study aims to establish criteria for the operational efficiency of urban low-floor electric buses of various types, considering autonomous DEE and their charging or replacement systems. These criteria should account for structural mass and nominal passenger capacity, facilitating a proper comparative assessment and the selection of the most optimal options for use in urban passenger transportation systems. Methodology. Criteria for the operational efficiency of city electric buses of various types are proposed based on an analysis of electricity consumption required for both movement and the operation of heating, ventilation, and air conditioning systems. The criteria consider the mass in the equipped state and the main parameter - nominal passenger capacity. Results. Several operational efficiency criteria for urban electric buses, incorporating mass parameters and passenger capacity, are suggested. The feasibility of their application is demonstrated through a comparison of five sketch projects of electric buses of various types with modern models. Originality. The proposed criteria for the operational efficiency of urban electric buses offer a method for adequately comparing any selected models, regardless of their types and technical specifications. Practical value. The proposed criteria for the operational efficiency of urban electric buses of various types can be beneficial for transport company specialists in selecting suitable rolling stock for specific routes. Additionally, design bureau and scientific and technical center specialists can utilize these criteria during the design process of new urban electric transport models.

Hybrid forecasting model of building cooling load based on combined neural network

The inefficient operation of air-conditioning system, which leads to its high-energy consumption, has aroused far-ranging concern at present. And an accurate prediction of building cooling load can be conducive to matching the supply and demand of the air-conditioning system, thus improving its operational efficiency. Therefore, a hybrid forecasting model (BAS-GRNN&LSTM) combing generalized regression neural network (BAS-GRNN) and long short-term memory neural network (BAS-LSTM) optimized by beetle antennae search algorithm is proposed for building cooling load prediction. First, the factors affecting the cooling load are comprehensively analyzed, and the random forest combined with recursive feature elimination (RF-RFE) method is used for feature selection. Then, the BAS-GRNN&LSTM model is developed to forecast. Finally, a simulation experiment is carried out using the measured data of a large building in north of China. Compared with GRNN, LSTM, BAS-GRNN and BAS-LSTM, BAS-GRNN&LSTM performs better in five performance evaluation metrics, RMSE, MAPE, RRE, MBE and R2, demonstrating its higher prediction accuracy. Furthermore, the detailed algorithm performance analysis shows that the proposed hybrid model has significant advantages in robustness, generalization capability and computational complexity, and can be well applied to building cooling load prediction and beneficial to the optimal control of building air-conditioning system.

Information entropy analysis of the relation between climate and thermal adaptation: A case study in hot summer and cold winter region of China

The shift of paradigm from reductionist to holism in the field of thermal adaptation promoted a boom in thermal adaptation theory and provided a systemic perspective on this issue. The thermal adaptation theory confronts twin difficulties of scale and complexity. The complexity and entropy of occupants' thermal adaptation emerging. This paper aims to investigate the information entropy exchange in the coupled climate-building-occupants system. First, we justified that the building-occupants system is an open system and exchanges energy, matter, and entropy with its surrounding climate system. Then, correlation analysis results suggested that climate entropy could influence thermal adaptation entropy. Thermal adaptation entropy fluctuated with climate entropy and the difference varied between −0.5 and 0.5 below 31 °C. A generalized entropy balance model was proposed for quantifying the entropy exchange among coupled climate-building-occupants systems for both free-running and air-conditioning buildings. The quantitative relation between the entropy of thermal adaptation and H_DTR reveals the dynamic occupant's thermal adaptation under the process of entropy exchange with the climate system. The H_DTR and entropy relation models provide a new perspective on thermal adaptation coupled with climate beyond outdoor daily mean temperatures. Control strategies that integrate the dynamic characteristics of DTR can bring more economical, energy-efficient and comfortable effects to the operation of air conditioning or heating systems. Our findings shed some light on the dynamical and complex thermal adaptation. In further studies, more results and methods from meteorological research could be used for thermal adaptation research and interdisciplinary theories could be adopted to help us better understand occupants' thermal adaptation.

Modified efficiency-NTU method (m-ε-NTU) for calculating air coolers in dehumidifying or frost conditions. Part IV

In the fourth part of the article, the algorithm for applying the newly developed the m-ε-NTU method for calculating air coolers in dehumidifying or frost conditions is described step by step, and its comparison with the method of segmented division of the heat exchanger is also given. This comparison showed good convergence of the calculation results with a multiple reduction in their execution time. The value of the deviation of the calculated value of thermal power calculated using the newly developed method from the same value calculated using the method of segmented division averaged 3.23% modulo and does not exceed 4.5% modulo. When the heat exchanger is divided into 40 segments, the execution time of the calculation programs increases approximately 18 times compared to using the newly developed method, which can be called a significant advantage of the latter. Considering the above, the newly developed method can be widely used for the selection of air coolers, their verification and design calculations.
 BACKGROUND: A universal method for calculating air coolers that is applicable to design and verification calculations is necessary. The method should consider the effect of dehumidification and frosting on the heat exchange process and allow the quick performance of many calculations to simulate the operation of refrigeration and air conditioning systems without significant loss of accuracy. However, this example of calculation method that addresses all the above-mentioned criteria is unavailable in the domestic and foreign literature.
 AIM: This study develops a universal method for calculating air coolers that is applicable to design and verification calculations. This method considers the influence of dehumidification and frosting on the heat exchange process and allows the quick performance of many calculations to simulate the operation of refrigeration and air conditioning systems without significant loss of accuracy.
 MATERIALS: The developed method for calculating air coolers is based on the classical approach of ε-NTU (efficiency– the number of heat transfer units) and is its adaptation, allowing consideration of the influence of dehumidification and frosting on the heat exchange process and performing the calculation (including the combined operating mode of the air cooler) without dividing the heat exchanger into separate segments. The estimation of the error of calculations performed using the developed method was conducted by comparing the calculated values of the thermal power of the device with those using the segmented division method for various operating modes (including combined).
 RESULTS: Comparison with the segmented division method of the heat exchanger showed good convergence of the calculation results with multiple reductions in execution time. The deviation value of the calculated value of the thermal power computed using the developed method from that using the segmented division method averaged 3.23% modulo and did not exceed 4.5% modulo. When the heat exchanger was divided into 40 segments, the execution time of the calculation programs increased approximately 18 times compared to using the developed method, which can be called a significant advantage of the latter.
 CONCLUSION: The division of the heat exchanger into segments for calculation does not result in a significant increase in accuracy compared with the new method. Thus, the developed m-ε-NTU method can be widely used for the selection of air coolers, their verification, and design calculations.

A global optimization method for data center air conditioning water systems based on predictive optimization control

As the national digitization process accelerates, the number and energy consumption of data centers are increasing year by year. Want to reduce the PUE of the data center. It is correct to start from the perspective of optimizing the energy consumption of air conditioning system operation. In this paper, a predictive optimization control method that is different from previous real-time optimization control is proposed. Predict, optimize, control and adjust the operating parameters of the air conditioning water system in the data center. At the same time, it solves the energy saving problem of the air conditioning system and the lag problem of previous adjustments. Predictive optimization control has broad prospects in research on operation optimization of data center air conditioning water systems.

Energy efficiency optimization analysis for a cooling composite air conditioning system for internet data centers

In this study, a validated mathematical model for a composite air conditioning system was established based on onsite test data. The system consists of a refrigerant pump driven two-phase cooling loop (RPTCL) and vapor compression refrigeration (VCR) cycles. The system can be switched to refrigerant pump (RP) mode, compressor mode, or hybrid mode, according to the outdoor temperature. By analyzing, the inadequacy of the existing Internet Data center (IDC) air conditioning system operation strategy was confirmed, and subsequently, five optimization strategies were proposed. The optimization strategies took two identical composite air conditioning systems as optimization unit, and under the premise of meeting the demand for cooling capacity, maximizing the operating time of RP and hybrid mode, which with higher energy efficiency, through complementary cooling capacity, to reduce the energy consumption. Meanwhile, when the cooling supply was higher than the demand, paired with optimization unit’s ON/OFF ratio control, the unit’s energy consumption was further reduced. The most optimization strategy provided 525.6 MWh of cooling energy to meet demand while consuming a minimum of 182.5 MWh. The optimization strategy also achieved a minimum of 12.1% and a maximum of 52.5% energy savings for IDCs in other climate zones of China.

Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor

Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%).

Smoothing cooling demand of buildings with PCM thermal batteries

Escalating energy tariffs and peak cooling demands due to climate change along with expanding use of variable renewable energy supply are presenting new challenges and opportunities for air conditioning system operation and control. This research presents the outcome of an investigation into the use of a thermal battery using salt-hydrate phase change material (PCM) in commercial buildings. A 1.2 m3 modular thermal battery using 15 °C melting temperature salt-hydrate PCM has been designed and fabricated. Its cooling performance and feasibility of integration into a chilled water-cooling system of commercial buildings has been comprehensively investigated. This storage unit can accommodate approximately 52 kWh of energy, featuring a rapid heat discharge rate of 32.58 kW during the initial 30 min to effectively address sudden cooling demands. The overall heat discharge rate closely aligns with simulation results, reaching approximately 96% accuracy. This has been achieved through optimisation of the heat exchanger design through mathematical simulation, detailed testing to match various operational scenarios and evaluation of economic and peak load shifting benefits. The results demonstrate the environmental and economic effectiveness of the PCM thermal battery as an independent component in building cooling systems. It provides a timely response to peak cooling demand and improves thermal comfort of the buildings.

Parametric identification method for an absorption air conditioning parabolic trough collector solar plant

In this work is established a parametric identification method for an absorption air conditioning solar plant. A scaled thermal plant, consisting of a thermal capacitor and a flow line that acts as a capacitor and thermal energy radiator is used. As the mathematical model of the scaled plant is structurally identical to that of the solar plant the first is used to determine the methodology that can be used later for the identification of the PTC solar plant. Parametric identification is a necessary step that allows to determine the unknown parameters of the mathematical model of any solar/thermal plant. This model then can be used to analyze the plant characteristics and design an appropriate control algorithm. Although the system model is nonlin-ear it can be expressed in the form of a linear regressor in the parameters. This permits to use the least squares method as the identification method. The method is applied to the thermal plant to identify the useful form that the covariance matrix and excitation signals should have to ensure that when applied to the solar plant its unknown parameters can be properly estimated. Once the solar plant parameters are properly estimated model can be used to analyze and simulate the operation of the ab-sorption air conditioning system.

Flexibility potential quantification and regulation measure comparison for the building air-conditioning system

Air-conditioning (AC) systems are the resources with the greatest flexibility potential for building demand response (DR). However, there is no unified method to quantify its flexibility under different DR measures, which results in difficulties in fully utilizing the DR potential and determining a proper regulation measure for the AC system operation strategies. To bridge this gap, the indicators of energy flexibility and power flexibility are firstly proposed in this study to compare three measures, active storage, passive storage, and temperature resetting, during the DR process of AC systems. According to the regulation measures with quantified flexibility potentials, different DR regulation measures are combined to explore the maximized stack flexibility. Based on a case study of an office building in Tianjin, the results show that among the different stacking strategies, stacking the active energy storage and temperature reset strategies produces the lowest operating cost, which is 13.8% and 22.5% lower than the stacking all three measures’ operating cost and total energy consumption, respectively. In the case of stacking the two measures, the highest energy flexibility is achieved when stacking the active and passive energy storage measure. If the temperature reset measure is further stacked, the energy flexibility can be increased by 15%.

Research on fault diagnosis strategy of air-conditioning system based on signal demodulation and BPNN-PCA

An efficient fault diagnosis strategy is crucial for the operation of air conditioning systems. In this paper, a novel fault diagnosis strategy based on the signal demodulation method and deep learning model is proposed for a small sample set's fault diagnosis. The potential application of the novel method in air conditioning systems has been discussed through three fault experiments and model evaluation indicators. The signal demodulation method based on principal component analysis (DPCA) is applied in the field of image enhancement innovatively. Compared with the time-domain sample set, the DPCA sample set has stronger features and higher discrimination. The correct rate of the model using the DPCA sample set has been improved by 10.44 %, the loss rate has been reduced by 34.68 %, and the running time has been reduced by 57.13 %. The Back Propagation Neural Network- Principal Component Analysis (BPNN-PCA) model is applied to air conditioning fault diagnosis. Compared with the traditional BPNN and the CART model, the BPNN-PCA model has better fault diagnosis performance and computational efficiency. Through the test and the model performance evaluation indicators, the model optimization strategy has been proposed, and its effectiveness has been verified. This lays the foundation for the optimization and improvement of the model.

Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach

With global warming intensifying and resource conflicts escalating, the world is undergoing a transformative shift toward sustainable practices and energy-efficient solutions. With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). Within a BEMSplatform, regulating the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is more important, noting that HVAC systems account for about 40% of the total energy cost in the commercial sector.This paper offers a Deep Reinforcement Learning (DRL) algorithm as a data-driven approach to controlling HVAC operation to enhance the energy efficiency of commercial buildings with open offices while ensuring thermal comfort for occupants in different zones. Compared to alternative methods such as rule-based models and model-predictive control, data-driven models have shown promising results in optimizing building energy consumption without the need for building-specific thresholds, prior knowledge about the underlying physics of heat distribution, and digital mapping of the airflow. Despite the astonishing performance of modern DRL methods in controlling energy management, a particular energy-saving solution for open-plan offices with multiple Variable Air Volume (VAV) systems, where different zones cannot be treated independently, is still missing. Also, some of the existing methods suffer from key issues such as long training time and lack of generalizability for using over-complicated models, incorporating external factors that are hard to model and characterize, and including factors that are not typically accessible. To solve these issues, we propose a low-complexity DRL-based model with multi-input multi-output architecture for the HVAC energy optimization of open-plan offices, which uses only a handful of controllable and accessible factors. The efficacy of our solution is evaluated through extensive analysis of the overall energy consumption and thermal comfort levels compared to a baseline system based on the existing HVAC schedule from a real case. This comparison shows that our method achieves 37% savings in energy consumption with minimum temperature violation (<1%) of the desired temperature range during work hours. It takes only a total of 40 min for 5 epochs (about 7.75 min per epoch) to train a network with superior performance and covering diverse conditions for its low-complexity architecture; therefore, it easily adapts to changes in the building setups, weather conditions, occupancy rate, etc. Moreover, by enforcing smoothness on the control strategy, we suppress the frequent and unpleasant on/off transitions on HVAC units to avoid occupant discomfort and potential damage to the system. The generalizability of our model is verified by applying it to different building models and under various weather conditions.

Intelligent fault diagnosis for air handing units based on improved generative adversarial network and deep reinforcement learning

Data-driven Automatic fault detection and diagnosis (AFDD) for air handling units (AHUs) is crucial for ensuring the stable operation and energy consumption of the heating ventilation air-conditioning (HVAC) system. However, traditional machine learning methods often underperform when confronted with insufficient training sample data, especially when lacking samples from the fault types. Based on the issues of insufficient samples from the fault types and imbalanced training dataset, this study proposes a novel AFDD approach using transformer integrated conditional Wasserstein generative adversarial network and deep reinforcement learning (TCWGAN-DRL) to synthesize the fault data and select high quality synthetic data samples. Firstly, we utilize the proposed TransCWGAN to synthesize fault samples. Then, reinforcement learning is utilized to select high quality synthetic samples. Finally, the filtered samples and the real fault samples are merged to form the training dataset for conventional supervised learning classifiers. Experimental results demonstrate that the enriched training dataset can effectively improve the AFDD results and outperforms recently published existing methods, for instance, compared to the suboptimal model, our method exhibits an increase in fault recognition accuracy of 4.9%, 3.66%, and 4.02% when the number of real fault samples is 15, 20, and 30, respectively.

Coupling BIM and detailed modelica simulations of HVAC systems in a common data environment

In current building design practices, the operation and performance of heating, ventilation and air conditioning (HVAC) systems are rarely documented in detail. This is mainly caused by the manual burden of generating detailed HVAC simulation models. To lower the barrier for detailed HVAC simulations, this paper presents an automated toolchain that generates and simulates models in Modelica language utilizing building information structured in a web-based common data environment. This approach differs from previous approaches by its focus on HVAC systems and integration with a common data environment over file-based BIM, which increases interoperability and allows users to run simulation studies in the cloud. The tool successfully generated and simulated a model of the HVAC systems in a small building under ideal and faulty operation of the heating system and thus demonstrated a fully interoperable data exchange between a common data environment and a simulation environment while showcasing the potential of analyzing HVAC systems with Modelica.

Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition

Load forecasting has significant implications on optimizing the operation of air conditioning systems for industrial mushroom houses and energy saving. This research paper presents a novel approach for short-term load forecasting in mushroom houses, which face challenges in accurately modeling cold and heat loads due to the complex interplay of various factors, including climatic conditions, mushroom growth, and equipment operation. The proposed method combines empirical wavelet transform (EWT), hybrid autoregressive integrated moving average (ARIMA), convolutional neural network (CNN), and bi-directional long short-term memory (BiLSTM) with an attention mechanism (CNN-BiLSTM-Attention) to address these challenges. The first step of this method was to select input features via the Boruta algorithm. Then, the EWT method was used to decompose the load data of mushroom houses into four modal components. Subsequently, the Lempel–Ziv method was introduced to classify the modal components into high-frequency and low-frequency classes. CNN-BiLSTM-Attention and ARIMA prediction models were constructed for these two classes, respectively. Finally, the predictions from both classes were combined and reconstructed to obtain the final load forecasting value. The experimental results show that the Boruta algorithm selects key influential feature factors effectively. Compared to the Spearman and Pearson correlation coefficient methods, the mean absolute error (MAE) of the prediction results is reduced by 14.72% and 3.75%, respectively. Compared to the ensemble empirical mode decomposition (EEMD) method, the EWT method can reduce the decomposition reconstruction error by an order of magnitude of 103, effectively improving the accuracy of the prediction model. The proposed model in this paper exhibits significant advantages in prediction performance compared to the single neural network model, with the MAE, root mean square error (RMSE), and mean absolute percentage error (MAPE) of the prediction results reduced by 31.06%, 26.52%, and 39.27%, respectively.

Data-driven online energy management framework for HVAC systems: An experimental study

The optimization of the operation of heating, ventilation, and air conditioning (HVAC) systems has gained considerable attention due to its energy conservation potential. Collecting various building operation data presents an opportunity to improve the efficiency of energy management and reduce consumption. In this paper, a data-driven online energy management framework is proposed for enhancing the effectiveness of HVAC systems and minimizing energy consumption. To streamline the modeling process and improve the accuracy of temperature estimation, our proposed framework adopts a symbolic regression approach to estimate building thermodynamics based on the collected data. It also formulates a model predictive control (MPC)-based HVAC scheduling strategy to optimize HVAC operations while maximizing thermal comfort and reducing energy consumption and peak power demand. We conducted on-site experiments to evaluate the proposed framework and demonstrated a notable reduction in total energy consumption in both HVAC cooling and heating operations: average reductions of 4.9% and 30.2%, and 49.3% and 73.9% for best cases in the on-site experiment under certain conditions. In the heating operation, the proposed data-driven HVAC management framework efficiently utilized indoor/outdoor heat generation to maintain comfortable temperature levels without activating the HVAC. Such effective utilization of heat sources shortened the HVAC runtimes and further reduced the total energy consumption. With the proposed framework, the average HVAC peak power demand fell by 25.8% and 35.1% during cooling and heating operations in the on-site experiment with certain scenarios. On-site experiment results demonstrated the proposed framework can efficiently suppress peak power demand, reduce energy consumption in both cooling and heating operation under certain conditions. The framework’s data-driven approach provides an effective means of optimizing HVAC operations, reducing energy consumption, and improving the thermal comfort of the building’s occupants.

Research on energy consumption analysis of air conditioning and refrigeration equipment based on perceptron model

Abstract This paper mainly studies the energy consumption of air conditioning and refrigeration system from the perspective of the dynamic operation of air conditioning systems and selects the energy consumption of a chiller system, cooling tower system, chilled water pump system and fan coil system, which affect the energy consumption of air conditioning system, as model variables. An integrated learning method based on a multilayer perceptron is proposed to integrate spatial features with temporal features, and a spatio-temporal prediction model for air conditioning energy consumption analysis is established under the Seq2Seq framework, and then the input curves of the four variables with load change under the optimal operation of the air conditioning system under dynamic load are derived. The results show that the deviation between the simulated value and the measured, calculated value of the energy consumption analysis model of air conditioning and refrigeration equipment based on the multilayer perceptron is within 5%, which can effectively and accurately predict and reduce air conditioning energy consumption by 15% to 20%. The maximum energy-saving efficiency can reach 30% when the air conditioning system is in optimal operation under dynamic load, and the energy-saving effect of the chiller, chilled water pump and cooling water pump is relatively significant. The research results of this paper have a certain reference value for reducing the energy consumption of air conditioning systems in practice.

Real-time energy consumption prediction method for air-conditioning system based on long short-term memory neural network

With increasing energy consumption, how to achieve the energy-saving operation of air-conditioning systems is crucial for improving the energy efficiency of buildings. The accurate and reliable energy consumption prediction of air-conditioning systems can be useful for optimizing the energy supply and equipment operation strategies. However, most existing studies focus on the prediction of the long-term energy consumption of air-conditioning systems, which usually exceeds the individual control time steps of the air-conditioning system. If the energy consumption of air-conditioning systems in the next control step can be predicted, then the least energy-consuming operating strategy can be selected at the beginning of each control step by relying on the predicted energy consumption, thereby achieving greater energy conservation. This paper proposes a real-time energy consumption prediction model for air-conditioning systems based on a long short-term memory neural network. The correlation between the energy consumption of air-conditioning system and input variables can be obtained by calculating the Spearman correlation coefficient. The predicted model results show that the normalized root mean square error of the prediction model is 0.0429 when the indoor air temperature, condenser inlet temperature, and historical energy consumption are set as inputs to predict the energy consumption of the air-conditioning system for the next 15 min. Meanwhile, the energy consumption at a certain indoor temperature setpoint can be predicted by the prediction model to aid in selecting a more appropriate indoor temperature setpoint, which can reduce energy consumption by optimizing the operation strategy of the air-conditioning system at each control step.

Optimization-informed Rule Extraction for HVAC system: A Case Study of Dedicated Outdoor Air System Control in a Mixed-Humid Climate Zone.

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control.